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Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments

Ferrarini, Bruno and Milford, Michael J and McDonald-Maier, Klaus D and Ehsan, Shoaib (2022) 'Binary Neural Networks for Memory-Efficient and Effective Visual Place Recognition in Changing Environments.' IEEE Transactions on Robotics, 38 (4). pp. 1-15. ISSN 1552-3098

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Abstract

Visual place recognition (VPR) is a robot’s ability to determine whether a place was visited before using visual data. While conventional handcrafted methods for VPR fail under extreme environmental appearance changes, those based on convolutional neural networks (CNNs) achieve state-of-the-art performance but result in heavy runtime processes and model sizes that demand a large amount of memory. Hence, CNN-based approaches are unsuitable for resource-constrained platforms, such as small robots and drones. In this article, we take a multistep approach of decreasing the precision of model parameters, combining it with network depth reduction and fewer neurons in the classifier stage to propose a new class of highly compact models that drastically reduces the memory requirements and computational effort while maintaining state-of-the-art VPR performance. To the best of our knowledge, this is the first attempt to propose binary neural networks for solving the VPR problem effectively under changing conditions and with significantly reduced resource requirements. Our best-performing binary neural network, dubbed FloppyNet, achieves comparable VPR performance when considered against its full-precision and deeper counterparts while consuming 99% less memory and increasing the inference speed by seven times.

Item Type: Article
Uncontrolled Keywords: Binary neural networks; localization; visual-based navigation
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 Jun 2022 16:07
Last Modified: 09 Aug 2022 06:34
URI: http://repository.essex.ac.uk/id/eprint/33071

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